When the species–time–area relationship meets island biogeography: Diversity patterns of avian communities over time and space in a subtropical archipelago
Bibliographic record
Abstract
Abstract Aim The species–area ( SAR ) and species–time relationships ( STR ) are of vital importance in community ecology. Previous studies suggest that a unified, general species–time–area relationship ( STAR ) may hold, with non‐independent scaling of richness across space and time. Most STAR studies to date have considered species accumulation curves in relatively homogeneous habitats. Here, we test the generality of the STAR in an island system and assess how factors other than area influence species richness, accumulation and turnover through time. Location Thousand Island Lake, China. Methods We surveyed bird communities on 36 islands using line transects, and calculated annual species richness of breeding birds from 2007 to 2015. We built island STAR models at island (island STAR ; ISTAR ) and transect levels (local community–time–area relationship; LCTAR ). We employed partial correlations and multiple regressions to examine potential influences of island attributes other than area (i.e. isolation, edge effect and habitat richness) on slopes of STR s. Results ISTAR and LCTAR models explained 88.8% and 83.1% of total variance, respectively, and both models have a negative space–time interaction. Richness scales comparably in space and time, for both whole‐island and transect‐level analyses. The partial correlation analysis showed that distance to mainland and perimeter‐to‐area ratio are significantly positively correlated with the time scalar ( w ), and habitat richness and w are negatively correlated. Multiple regression models identify perimeter‐to‐area ratio as particularly influential. Main conclusions The STAR pattern generalized to an island system where species turnover is high, indicating an interdependency of time and space in determining species richness. Islands have attributes other than area that influence patterns of species accumulation and turnover through time. Ecologists should consider the interdependence of space and time when characterizing species richness patterns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".